基于加工位置不确定的多工步数控铣削工艺参数优化研究

Process parameters optimization of multi-pass CNC milling considering uncertain machining position

针对铣削稳定性评价指标极限切削深度随加工位置改变而变化,导致铣削工艺参数优化模型中稳定性约束具有不确定性问题,结合不同加工位置刀具频响函数和切削稳定性理论,建立加工空间极限切削深度广义回归神经网络(GRNN)预测模型,基于该GRNN模型完善铣削稳定性约束条件,进而构建以机床各运动部件位移与粗/精加工切削参数为变量,以粗/精加工总切削时间为目标的多工步数控平面铣削工艺参数优化模型,采用粒子群算法(PSO)求解该优化模型。以某企业加工中心展开实例研究,获取机床加工位置和粗/精加工主轴转速、切削深度、切削宽度、每齿进给量的优化配置,优化后粗/精加工总切削时间比优化前缩短22.47%,并通过该配置下的无颤振铣削加工验证了优化模型的有效性。

Limiting cutting depth for evaluating the milling stability is dependent on the machining position.The consequence is that the stability constraint of the process parameters optimization model has uncertain.To solve this problem,the tool tip frequency response functions at different machining positions are combined with the milling stability theory.Firstly,a general regression neural network(GRNN)is formulated for predicting the position-dependent limiting cutting depth,which can be used to determine the milling stability constraint.Then,a process parameters optimization model of multi-passes milling for minimizing cutting time is established.Displacements of the machine tool moving parts and cutting parameters for rough and finish milling processes are taken as variables.The particle swarm optimization algorithm(PSO)is utilized to solve this optimization model.A case study is implemented on a vertical machining center.The optimal combination of machining position and cutting parameters can be obtained,including the spindle speed,cutting depth,cutting width and feed rate per tooth.The total cutting time of the rough and finish processes decreases 22.47%after the optimization.There is no chatter during the milling process,which verifies the feasibility of the proposed optimization model.